Method and apparatus for identifying a representative area of an image

ABSTRACT

A computer implemented method for generating a representative thumbnail for an image. The method comprises determining a representative area of an image, the determining comprising determining an absence of faces in the image; dividing the image into one or more zones; and selecting a zone with maximum edge strength as the representative area; and generating a thumbnail by cropping the image to the representative area.

BACKGROUND

1. Field of the Invention

Embodiments of the present invention generally relate to image viewingand, more particularly, to a method and apparatus for identifying arepresentative area for an image.

2. Description of the Related Art

Images, such as digital photographs, can be taken in different sizes andin different aspect ratios (such as landscape, portrait, 6×4 inches, 7×5inches, etc.). In a collection of images, there are often a combinationof images with different sizes and aspect ratios mixed together. Whendisplaying thumbnail images of the collection in a grid created by photoorganization software tools, display space is wasted or images arecropped to generate uniformly sized thumbnails such as squares. Displayspace tends to be wasted because the photo organization tools resizeirregularly sized images to fit into a pre-allocated grid space with adifferent aspect ratio than the resized images. Additionally, croppedthumbnail images tend to be “center” cropped, often resulting in aninaccurate representation of the original image.

Therefore, there is a need for a method and apparatus for identifying arepresentative area for an image.

SUMMARY

A method for identifying a representative area of an image. The methodcomprises determining a representative area of an image. Upondetermining an absence of faces in the image, the image is divided intozones. The zone with maximum edge strength is selected as therepresentative area. A thumbnail is generated by cropping the image tothe representative area.

In another embodiment, an apparatus for identifying a representativearea of an image is described. The apparatus comprises a computer havingone or more processors for executing instructions comprising a facedetection module configured to detect that a scaled image includes oneor more faces, and determine a priority for each of the one or morefaces. The apparatus also includes an image processing module configuredto select a representative area that includes the one or more faces,when the priority of each of the one or more faces is greater than apredefined threshold. The image processing module then generates athumbnail by cropping the image to the representative area.

In yet another embodiment, a non-transient computer readable medium forstoring computer instructions that, when executed by at least oneprocessor causes the at least one processor to perform the methodidentifying a representative area for an image is described.

The Summary is provided to introduce a selection of concepts in asimplified form that are further described below in the DetailedDescription. This Summary is not intended to identify key features oressential features of the claimed subject matter, nor is it intended tobe used as an aid in determining the scope of the claimed subjectmatter.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of a system for identifying a representativearea for an image, according to one or more embodiments;

FIG. 2 depicts a flow diagram of a method for identifying arepresentative area for an image as performed by the image processingmodule of FIG. 1, according to one or more embodiments;

FIG. 3 depicts a flow diagram of a method for detecting regions ofimportance in the scaled image, as performed by the image processingmodule of FIG. 1, according to one or more embodiments;

FIG. 4 depicts a flow diagram of a method for identifying faces in animage to generate a representative area of an image as performed by theface detection module of FIG. 1, according to one or more embodiments;

FIG. 5 depicts a flow diagram of a method for generating arepresentative area in an image with no detected faces as performed bythe representative rectangle module of FIG. 1, according to one or moreembodiments; and

FIG. 6 illustrates an example of an image and its representativethumbnail image, according to one or more embodiments.

While the method and apparatus is described herein by way of example forseveral embodiments and illustrative drawings, those skilled in the artwill recognize that the method and apparatus for generating ordered userexpert lists for a shared digital document is not limited to theembodiments or drawings described. It should be understood, that thedrawings and detailed description thereto are not intended to limitembodiments to the particular form disclosed. Rather, the intention isto cover all modifications, equivalents and alternatives falling withinthe spirit and scope of the method and apparatus for generating ordereduser expert lists for a shared digital document defined by the appendedclaims. Any headings used herein are for organizational purposes onlyand are not meant to limit the scope of the description or the claims.As used herein, the word “may” is used in a permissive sense (i.e.,meaning having the potential to), rather than the mandatory sense (i.e.,meaning must). Similarly, the words “include”, “including”, and“includes” mean including, but not limited to.

DETAILED DESCRIPTION OF EMBODIMENTS

Embodiments of the present invention include a method and apparatus foridentifying a representative area for an image. An image, such as aphotograph, is processed to generate a rectangle which best representsthe image contents as a thumbnail image. In some embodiments, if one ormore faces appear in the image, a rectangle which can fit around themost faces, or the most prominent and important faces, is selected as arepresentative area for the image. If no faces appear in the image, edgedetection is performed on the image. The image is divided into one ormore zones, and the zone that is determined to have the maximum edgestrength is selected as the representative area. The image is croppedbased on the representative area to generate a thumbnail image whichrepresents the contents of the image. The thumbnail image is used torepresent the image in a grid of images, e.g. in a photo gallery, sothat a viewer of the grid can easily ascertain the importance of theimage.

In another example, a set of filters may each apply individual effectsto the photograph and an embodiment of the present invention improvesthe user's ability to see how the filter impacts the most importantregions of the photograph and to subsequently select their favoriteeffect in light of the previewed result. In one instance, a grid ofthumbnail images may be displayed, each thumbnail having a distinctfilter applied to it. The representative area shown in the thumbnail iscalculated as described above, while the application of the filterallows the user to preview the effect of the filter effect withoutapplying it to an original image.

Advantageously, using the embodiments of the invention described herein,software applications such as ADOBE® PHOTOSHOP®, PHOTOSHOP ELEMENTS®,and the like provide meaningful representative thumbnails to users.

Various embodiments of a method and identifying a representative areafor an image are described. In the following detailed description,numerous specific details are set forth to provide a thoroughunderstanding of claimed subject matter. However, it will be understoodby those skilled in the art that claimed subject matter may be practicedwithout these specific details. In other instances, methods, apparatusesor systems that would be known by one of ordinary skill have not beendescribed in detail so as not to obscure claimed subject matter.

Some portions of the detailed description that follow are presented interms of algorithms or symbolic representations of operations on binarydigital signals stored within a memory of a specific apparatus orspecial purpose computing device or platform. In the context of thisparticular specification, the term specific apparatus or the likeincludes a general-purpose computer once it is programmed to performparticular functions pursuant to instructions from program software.Algorithmic descriptions or symbolic representations are examples oftechniques used by those of ordinary skill in the signal processing orrelated arts to convey the substance of their work to others skilled inthe art. An algorithm is here, and is generally, considered to be aself-consistent sequence of operations or similar signal processingleading to a desired result. In this context, operations or processinginvolve physical manipulation of physical quantities. Typically,although not necessarily, such quantities may take the form ofelectrical or magnetic signals capable of being stored, transferred,combined, compared or otherwise manipulated. It has proven convenient attimes, principally for reasons of common usage, to refer to such signalsas bits, data, values, elements, symbols, characters, terms, numbers,numerals or the like. It should be understood, however, that all ofthese or similar terms are to be associated with appropriate physicalquantities and are merely convenient labels. Unless specifically statedotherwise, as apparent from the following discussion, it is appreciatedthat throughout this specification discussions utilizing terms such as“processing,” “computing,” “calculating,” “determining” or the likerefer to actions or processes of a specific apparatus, such as a specialpurpose computer or a similar special purpose electronic computingdevice. In the context of this specification, therefore, a specialpurpose computer or a similar special purpose electronic computingdevice is capable of manipulating or transforming signals, typicallyrepresented as physical electronic or magnetic quantities withinmemories, registers, or other information storage devices, transmissiondevices, or display devices of the special purpose computer or similarspecial purpose electronic computing device.

FIG. 1 is a block diagram of an apparatus 100 for identifying arepresentative area for an image, according to one or more embodiments.The apparatus 100 includes a computer 102. The computer 102 is acomputing device, for example, a desktop computer, laptop, tabletcomputer, and the like. The computer 102 includes a Central ProcessingUnit (CPU) 104, support circuits 106, and a memory 108. The CPU 104 mayinclude one or more commercially available microprocessors ormicrocontrollers that facilitate data processing and storage. Thevarious support circuits 106 facilitate the operation of the CPU 104 andinclude one or more clock circuits, power supplies, cache, input/outputcircuits, and the like. The memory 108 includes at least one of ReadOnly Memory (ROM), Random Access Memory (RAM), disk drive storage,optical storage, removable storage and/or the like.

The memory 108 includes an operating system 110, an image processingmodule 112, a face detection module 114, a region detection module 116and a representative rectangle generation module (RRGM) 118. The regiondetection module 116 further includes an edge detection module 120. Theoperating system 110 may include various commercially known operatingsystems.

The image processing module 112 processes an image 120, e.g., aphotograph taken by a digital camera, to produce a thumbnailrepresentation 122 for the image 120. The image processing module 112may process an entire gallery of images/photographs from a mobiledevice, laptop, desktop computer, or the like. According to oneembodiment, the image processing module 112 scales the image 120 to asmaller scale image 121 (while maintaining an aspect ratio of image 120)before continuing with any further steps in order to reduce processingtime and burden on the underlying hardware.

For an image where there are no faces present in the image, the imageprocessing module 112 also subdivides the image 120 into a plurality ofzones so that the processing module 112 may compare the zones todetermine which zone is a best representative area of the image 120. Azone is a sub-region of the image 120. One or more zones may include therepresentative area of the image 120. (W,H) represents the width andheight, in pixels, of the image 120. (W1,H1) represent the height andwidth of the thumbnail rectangle area. The scaling factors along theheight and width are computed as: S1=H/H1 and S2=W/W1. If it isdetermined that S1 is equal to S2, the image 120 is scaled with eitherscaling factor to form the scaled image 121, and no zones need to becomputed. In other words, if the aspect ratio of the image 120 is equalto the aspect ratio of the thumbnail rectangular area, then no zones arecomputed. Rather the image 120 is scaled to the size of the thumbnailrectangular area. The scaled image 121 is used as a thumbnail image. If,however, the aspect ratio of the image 120 is not equal to the aspectratio of the thumbnail rectangular area, then zones are computed.According to one embodiment, if S1 is greater than S2, the image 120 issubdivided into zones, where for each zone, the width of the zone isgreater than the height of the zone. Conversely, if S2 is greater thanS1, the image 120 is subdivided into zones, where for each zone, theheight of the zone is greater than the width of the zone.

In one embodiment, the scaled image is formed into overlapping zoneswith a width greater than a height if S1 is greater than S2. The zonesare overlapped to produce representative areas which do not cut offimportant portions of objects in the image. For example, if the image120 is 2000×1000 pixels and the thumbnail must be sized down to a 200×50pixel window, the scaling factor for the height, S1 is determined as1000 divided by 50, or 20. The scaling factor for the width, S2 isdetermined as 2000 divided by 200, or 10. Since S1 is greater than S2,the lower scaling factor S2 is used to determine the zones. In thepresent example, each zone has a height of S2×H1, or 10×50=500. Eachzone has a width of S2×W1=10×200=2000.

In another embodiment, the scaled image 121 is formed into overlappingzones with a height greater than a width if S1 is less than S2. Forexample, given the same 2000×1000 pixel original image, but a thumbnailsize of 100×100 pixels, S1 is determined as 1000 divided by 100, or 10,and S2 is determined as 2000 divided by 100, or 20. Because S1 is lessthan S2, the lower scaling factor S1 is used. Accordingly the zone willbe formed as having a width equal to S1×W1=1000 and a height equal toS1×H1=1000. The RDM 116 divides the filtered image into vertical orhorizontal zones.

In some instances, the image 121 contains one or more people, or may bea picture of scenery or other non-human objects. The face detectionmodule 114 is invoked by the image processing module 112 to determinewhether the image contains faces. If the face detection module 114determines that one or more faces are found in the image, the faces aredetected and marked. Those of ordinary skill in the art will recognizethat standard face-detection methodologies are used herein. The facedetection module 114 then distinguishes between each of the one or morefaces detected by assigning a weight indicating an importance of eachface. For example, if one face is larger than several other faces, thelarger face is given a greater weight than the smaller faces. If severalfaces are blurred and one or two faces are more in focus, those one ortwo faces that are more in focus are given a greater weight. The greaterweight indicates that if the image were to be cropped to form athumbnail, those faces would be more important to show than smaller ormore blurred faces.

The RRGM 118 generates a representative area, e.g. a rectangle, whichincludes a maximum number of faces, or, alternatively, the mostimportant faces. In other embodiments, the maximum number of importantfaces may also be used as the basis for the generation of therepresentative area. In some embodiments, the RRGM module 118 can beconfigured with parameters that adjust a threshold of face weight toinclude in the generated representative area, such as focus area, or thelike. For example, if the threshold of weight is set low, then morefaces are included and the representative area may represent a largerportion of the original photograph/image. If the threshold of weight isset high, then less faces are included in the representative area, butthose included faces are more important than occluded faces. In someembodiments, the RRGM 118 generates a representative area that coversthe maximum number of faces. The number of faces to be covered may alsobe an adjustable parameter of the RRGM 118. In an instance where aphotograph contains an equal number of faces on an extreme of the sidesof the image, priority is given to larger faces over smaller faces.

If no faces are detected in the photograph, the image processing module112 invokes the region detection module (RDM) 116. The RDM 116determines whether the photograph contains foliage, structures,landscapes, or the like.

If no faces are detected in the image 121 and the RDM 116 determinesthat the photograph contains foliage, structures or the like, the scaledimage 121 is converted to a gray scale image, e.g., an image where eachcolor in the image 120 is represented in a range from black to white.The RDM 116 then filters the gray scale image. In some embodiments, thefilter may be a low pass filter, such as a Gaussian filter. Thefiltering reduces noise which may otherwise be detected as edges in thegrayscale image. Each zone in the filtered image is then processed bythe edge detection module (EDM) 120 to detect edges (e.g., thresholdsbetween objects, or the like) in the image. The EDM 120 employs an edgedetection filter such as a Sobel edge detector, for example.

The detection of many edges in a zone indicates more activity in theparticular zone. Generally, a zone where more edges are detected has ahigher strength of edges. A zone where fewer edges are detected, such asa clear sky, a plain background, or an out-of-focus region, has a lowerstrength of edges. The RRGM 118 then generates a rectangle with themaximum edge strength as the representative area. The rectangle mayinclude one or more zones according to an adjustable parameter in theRRGM 118. The image processing module 112 then crops the image 121 tothe thumbnail representation 122 based on the representative area. Insome embodiments, the representative area may have a width and heightequal to each other, generating a representative square.

The image processing module 112 can then generate a uniform aspect ratiogrid representation of non-uniform aspect ratio images. Each originalphotograph may be of different sizes and different aspect ratios,however the representative rectangle generation module (RRGM) 118generates a rectangle of uniform size for each photograph. The resultinggrid of thumbnail images shows the important regions of each image andno space is wasted, while outlining to a viewer the importance of eachimage.

FIG. 2 depicts a flow diagram of a method 200 identifying arepresentative area for an image as performed by the image processingmodule 112 of FIG. 1, according to one or more embodiments. The methodbegins at step 202 and proceeds to step 203.

At step 203, the method 200 determines whether the scaling factor of thewidth (S1) and the scaling factor for the height (S2) between a detectedimage and the desired thumbnail image size are equal. If the scalefactors S1 and S2 are equal, the method proceeds to step 211. At step211, the image is resized based on the scale factor between dimensionsof the detected image and dimensions of a desired thumbnail image. Forexample, if the thumbnail size is desired to be 100×50 pixels, a1200×600 pixel image will be scaled down by a factor of 12 to a size of100×50 pixels.

However, if, at step 203, the scale factors are not equal, the methodproceeds to step 204. At step 204, the method 200 scales an image orphotograph to produce a scaled image. This reduces processing time inthe further steps of the method 200. For example, if an image is4000×3000 pixels, the image may be reduced to 800×600 pixels, therebyreducing the number of pixels that the method is performed on, reducingprocessing time.

The method 200 then proceeds to step 206, where the method 200 detectsthe regions of importance in the scaled image. In some embodiments,regions of importance are determined by the faces detected in the image,as described in further detail with respect to FIG. 3 below. Forexample, if one area of an image contains many faces, the regions ofimportance are focused on that area. In other embodiments, a greateramount of activity determines where the regions of importance are, asdescribed in further detail with respect to FIG. 4 below. In aphotograph showing fireworks, the region of the photograph with thefireworks is determined to be a region of importance.

The method 200 proceeds to step 208. At step 208, the method 200generates a representative area containing the regions of importance. Asdescribed above, for example, the representative area may contain all ofthe detected faces. In another example, the representative area maycontain mountains, fireworks, vehicles, or the like.

The method 200 proceeds to step 210. At step 210, the method 200 cropsthe scaled image based on the representative area. As in the aboveexample, the scaled image is 800×600 pixels and the method 200determines that the representative area is a 600×600 pixels portion ofthe scaled image, starting from the top left of the image. Accordingly,the scaled image is cropped to the 600×600 pixels top-left portion, athumbnail representing the important portion of the original 4000×3000pixel image. Now, when this method 200 is applied across a set of imagessuch as a gallery, or in a grid-view, a user is easily able to see allof the images and why those images were taken based on the importantportions being shown. The method 200 proceeds to step 212 and ends.

FIG. 3 depicts a flow diagram of a method 300 for detecting regions ofimportance in the scaled image, as performed by the image processingmodule 112 of FIG. 1, according to one or more embodiments. The method300 starts at step 302 and proceeds to step 304.

At step 304, the method 300 scales the image to a desired thumbnailimage size. The method 300 calculates scaling factors for the width andthe height. (W,H) represents the width and height, in pixels, of theimage. (W1,H1) represents the height and width of the thumbnailrectangle area. The method 300 computes the scaling factors along theheight and width as: S1=H/H1 and S2=W/W1. If S1 is equal to S2, themethod 300 scales the image using either scaling factor to form thescaled image. In other words, if the aspect ratio of the image is equalto the aspect ratio of the thumbnail rectangular area, the method 300scales down the image by either scaling factor.

The method 300 resizes the image based on the scale factor betweendimensions of the detected image and dimensions of a desired thumbnailimage. For example, if the thumbnail size is desired to be 100×50pixels, a 1200×600 pixel image will be scaled down by a factor of 12 toa size of 100×50 pixels. In some embodiments, if the aspect ratio of theimage is equal to the aspect ratio of the thumbnail rectangular area,the method 300 scales down the image and returns the scaled down imagesas the generated thumbnail. The method 300 proceeds to step 316, andends. However, if the scale factors are not equal, the method 300 scalesthe image to an intermediate size in order to reduce processing timewhen determining a representative area of the image. For example, if animage is 4000×3000 pixels, the image may be reduced to 800×600 pixels.

The method 300 proceeds to step 306, where the method 300 detects if thescaled image includes at least one face. The method 300 performs a facedetection algorithm to determine is the image includes at least oneface. If the scaled image includes a face, it is assumed that therepresentative area of the image includes the face. If the scaled imagedoes not include a face, it is assumed that the representative area maybe anywhere in the image. The method 300 proceeds to step 308, where themethod 300 determines whether a face was found. If a face was found themethod 300 proceeds to step 310, where the method 300 identifies an areaof importance in the scaled image using face detection, as described infurther detail with respect to FIG. 4, below. The method 300 proceeds tostep 314.

However, if at step 308, the method 300 determines that the scaled imagedoes not include a face, the method 300 proceeds to step 312, where themethod 300 identifies an area of importance in the image using edgedetection techniques, as described with respect to FIG. 4 below. Themethod 300 proceeds to step 314.

At step 314, the method 300 generates a thumbnail by cropping the imageto the representative area. The method 300 generates a representativearea containing the areas of importance. The method 300 crops the scaledimage based on the representative area. As in the above example, thescaled image is 800×600 pixels and the method 300 determines that therepresentative area is a 600×600 pixels portion of the scaled image,starting from the top left of the image. Accordingly, the scaled imageis cropped to the 600×600 pixels top-left portion, a thumbnailrepresenting the important portion of the original 4000×3000 pixelimage. Now, when this method 200 is applied across a set of images suchas a gallery, or in a grid-view, a user is easily able to see all of theimages and why those images were taken based on the important portionsbeing shown. The method 300 proceeds to step 316 and ends

FIG. 4 depicts a flow diagram of a method 400 for identifying faces inan image to generate a representative area of an image as performed bythe face detection module 114 of FIG. 1, according to one or moreembodiments. The method begins at step 402 and proceeds to step 404.

At step 404, the method 400 detects one or more faces in an image. Themethod 400 may utilize any face detection software known in the art. Themethod 400 may detect one or more faces in the image. The location ofthe one or more detected faces is marked according to their pixellocation and stored for later use.

The method 400 then proceeds to step 406, where the method 400 comparesdetected faces to each other to determine a priority for each of the oneor more faces in order to determine which faces are more “important”.For example, the faces may be judged according to size, focus, location,or the like. Those faces which are larger are considered more importantthan those faces that are smaller. Those faces which are in focus areconsidered more important than blurred faces.

The method 400 proceeds to step 408. At step 408, the method 400determines whether the faces are in extremes of the image, i.e., on theleft side and the right side, or on the top and on the bottom. If thefaces all lie on extremes of the image, the method 400 proceeds to step410.

At step 410, larger faces are assigned a higher priority than smallerfaces from the one or more detected faces and the method 400 proceeds tostep 412. At step 412, the method 400 creates a representative areacovering faces with the higher priority. In some embodiments, faces withhigher priority are faces determined to have a priority greater than apredefined threshold. For example, the area may cover a left side of theimage, but not the right side because faces detected at the left side ofthe image are larger and therefore considered more important than facesdetected on the right side. The method terminates at step 416.

However, if at step 408, the method 400 determines that the faces arenot in the extremes of the image at step 408, the method proceeds tostep 414. At step 414, the method 400 creates a representative areacovering the maximum number of faces. The method 400 identifiescoordinates for a location in the image, for example, on a top left sideof a face on the left. The method 400 then identifies an extent beyondthe right side of the faces and an extent to below all of the faces. Forexample if at step 404, five faces are detected, the representative areais formed as a rectangle large enough to cover the extent that includesall five faces. The method terminates at step 416.

FIG. 5 depicts a flow diagram of a method 500 for generating arepresentative area in an image with no detected faces as performed bythe region detection module 116 of FIG. 1, according to one or moreembodiments. The method begins at step 502 and proceeds to step 504.

At step 504, the method 500 detects edges in an image, for example,using a Sobel edge filter or the like. Edge detection algorithms alsoidentify intensity values of each edge. An edge-map is generated showingwhere edges in the image exist. The existence of an edge is deemed toindicate an area of importance. Optionally, before detecting edges, theimage is converted to a gray scale image and the gray scale image isfiltered. According to some embodiments, a low-pass filter is applied tothe gray scale image.

The method 500 proceeds to step 506. At step 506, the image is dividedinto one or more zones as described above in reference to FIGS. 1 and 2.According to an exemplary embodiment, (H,W) represents the height andwidth, in pixels, of the original image. (H1, W1) represent the heightand width of the thumbnail rectangle area. The scaling factors along theheight and width are computed as: S1=H/H1 and S2=W/W1. If the method 500determines that S1 is equal to S2, the image is scaled with eitherscaling factor S1 or S2. The scaled image may be formed into overlappingzones horizontally if S1 is less than S2. Further, the scaled image maybe formed into overlapping zones vertically if S1 is greater than S2.Forming the scaled image into zones vertically means dividing the imageinto a plurality of images where boundary of a zone runs from a top edgeof the image to a bottom edge of the image. Similarly, forming thescaled image into zones horizontally means dividing the image into aplurality of images where boundary of a zone runs from a left edge ofthe image to a right edge of the image.

The method 500 proceeds to step 508. At step 508, a zone is selectedfrom the one or more zones as the representative area, wherein theselected zone has a maximum edge strength. In other words, the zonesthat include a highest intensity value are determined to identify arepresentative image. The method terminates at step 510.

FIG. 6 illustrates an example of an image and its representativethumbnail image, according to one or more embodiments. The originalphotograph 602 may be of a different size than other photographs in acollection or gallery. Alternatively, the photograph may be loaded in aphoto alteration application, where the output of one or more “Effects”can be previewed simultaneously. As shown in FIG. 6, the effectsside-pane 604 contains various effects such as saturating the image,converting the image to grayscale, focusing the image, and the like. Thephotograph 602 is previewed as a thumbnail image 606. The imageprocessing module 112 recognized that there was a face in the photograph602 and created a representative area around the face. The photograph602 was cropped to the representative area to show the important regionsof the photograph, enabling a user of the alteration application toconveniently see how the chosen effect impacts the most importantregions of the photograph 602, as opposed to the thumbnail 608.Thumbnail 608 is generated based on a method called center cropping,where a fixed width and length of pixels from the center are selected asthe representative portion of the image. The difference betweenthumbnail 606 and 608 is apparent—the person in thumbnail 608 is cutoff, while in thumbnail 606, the person is the primary focal point.

The grid in side-pane 604 represents uniformly generated thumbnails ofthe photograph 602. In other examples, different photos of differentsizes may be represented uniformly in a gallery grid of images. In thephotograph 602, if an effect is applied, a user would most likely wantto see how the face of the person photographed is impacted. Therefore,the thumbnail generation described above with references to FIGS. 1-4generates the previews shown in side-pane 604, showing how each effectwould impact the user's face. Alternatively, if the photograph 602contained an image of a boat, for example, the edges of the boat wouldbe detected as described in FIGS. 1 and 5, and the generated thumbnailwould show an image of the boat closely cropped, so that the user cansee the effect of a particular filter on the important region of thephotograph, i.e., the boat.

The embodiments of the present invention may be embodied as methods,apparatus, electronic devices, and/or computer program products.Accordingly, the embodiments of the present invention may be embodied inhardware and/or in software (including firmware, resident software,micro-code, etc.), which may be generally referred to herein as a“circuit” or “module”. Furthermore, the present invention may take theform of a computer program product on a computer-usable orcomputer-readable storage medium having computer-usable orcomputer-readable program code embodied in the medium for use by or inconnection with an instruction execution system. In the context of thisdocument, a computer-usable or computer-readable medium may be anymedium that can contain, store, communicate, propagate, or transport theprogram for use by or in connection with the instruction executionsystem, apparatus, or device. These computer program instructions mayalso be stored in a computer-usable or computer-readable memory that maydirect a computer or other programmable data processing apparatus tofunction in a particular manner, such that the instructions stored inthe computer usable or computer-readable memory produce an article ofmanufacture including instructions that implement the function specifiedin the flowchart and/or block diagram block or blocks.

The computer-usable or computer-readable medium may be, for example butnot limited to, an electronic, magnetic, optical, electromagnetic,infrared, or semiconductor system, apparatus, device, or propagationmedium. More specific examples (a non-exhaustive list) of thenon-transient computer-readable medium include the following: harddisks, optical storage devices, a transmission media such as thosesupporting the Internet or an intranet, magnetic storage devices, anelectrical connection having one or more wires, a portable computerdiskette, a random access memory (RAM), a read-only memory (ROM), anerasable programmable read-only memory (EPROM or Flash memory), anoptical fiber, and a compact disc read-only memory (CD-ROM).

Computer program code for carrying out operations of the presentinvention may be written in an object oriented programming language,such as Java®, Smalltalk or C++, and the like. However, the computerprogram code for carrying out operations of the present invention mayalso be written in conventional procedural programming languages, suchas the “C” programming language and/or any other lower level assemblerlanguages. It will be further appreciated that the functionality of anyor all of the program modules may also be implemented using discretehardware components, one or more Application Specific IntegratedCircuits (ASICs), or programmed Digital Signal Processors ormicrocontrollers.

The foregoing description, for purpose of explanation, has beendescribed with reference to specific embodiments. However, theillustrative discussions above are not intended to be exhaustive or tolimit the invention to the precise forms disclosed. Many modificationsand variations are possible in view of the above teachings. Theembodiments were chosen and described in order to explain the principlesof the present disclosure and its practical applications, to therebyenable others skilled in the art to best utilize the invention andvarious embodiments with various modifications as may be suited to theparticular use contemplated.

The methods described herein may be implemented in software, hardware,or a combination thereof, in different embodiments. In addition, theorder of methods may be changed, and various elements may be added,reordered, combined, omitted, modified, etc. All examples describedherein are presented in a non-limiting manner. Various modifications andchanges may be made as would be obvious to a person skilled in the arthaving benefit of this disclosure. Realizations in accordance withembodiments have been described in the context of particularembodiments. These embodiments are meant to be illustrative and notlimiting. Many variations, modifications, additions, and improvementsare possible.

Accordingly, plural instances may be provided for components describedherein as a single instance. Boundaries between various components,operations and data stores are somewhat arbitrary, and particularoperations are illustrated in the context of specific illustrativeconfigurations. Other allocations of functionality are envisioned andmay fall within the scope of claims that follow. Finally, structures andfunctionality presented as discrete components in the exampleconfigurations may be implemented as a combined structure or component.These and other variations, modifications, additions, and improvementsmay fall within the scope of embodiments as defined in the claims thatfollow.

While the foregoing is directed to embodiments of the present invention,other and further embodiments of the invention may be devised withoutdeparting from the basic scope thereof, and the scope thereof isdetermined by the claims that follow.

1. A computer implemented method comprising: determining arepresentative area of an image, the determining comprising: determiningan absence of faces in the image; dividing the image into one or morezones; and selecting a zone with maximum edge strength as therepresentative area; and generating a thumbnail by cropping the image tothe representative area.
 2. The method of claim 1, wherein determining arepresentative image comprises scaling the image.
 3. The method of claim2, wherein scaling comprises: determining a first scale factor as aheight of the image divided by a height of a size of a thumbnail image;determining a second scale factor as a width of the image divided by thewidth of the thumbnail image; determining that the first scale factor isnot equal to the second scale factor; and scaling the image using thefirst and the second scale factor to produce a scaled image.
 4. Themethod of claim 1, wherein determining further comprises: converting theimage to a grayscale image; applying a filter to produce a smoothedimage with reduced noise; and detecting edges in the image to produce anedge-detected image.
 5. The method of claim 1, wherein dividing theimage comprises: determining whether a first scale factor is greaterthan a second scale factor; dividing the image horizontally when thefirst scale factor is greater than the second scale factor; and dividingthe image vertically when the second scale factor is greater than thefirst scale factor.
 6. The method of claim 4, further comprisingcomputing a strength of edges for each zone.
 7. The method of claim 1,further comprising: generating a grid of cropped images with uniformaspect ratios.
 8. The method of claim 1, further comprising: generatingone or more previews of the image corresponding to one or more effectsthat can be applied to the image; and forming the one or more previewsinto a grid of uniform aspect ratio images.
 9. An apparatus comprising acomputer having one or more processors for executing instructionscomprising: a face detection module configured to: detect that a scaledimage includes one or more faces; determine a priority for each of theone or more faces; and an image processing module configured to: selecta representative area, wherein the representative area includes the oneor more faces, when the priority of each of the one or more faces isgreater than a predefined threshold; and generate a thumbnail bycropping the image to the representative area.
 10. The apparatus ofclaim 9, wherein the priority is determined based on at least one ofsize of a detected face, focus of a detected face, and position of adetected face.
 11. The apparatus of claim 9, wherein selecting furthercomprises: modifying the representative area to contain a majority ofthe one or more faces having the priority greater than the predefinedthreshold.
 12. The apparatus of claim 9, wherein the image processingmodule is further configured to: generate a grid of cropped images withuniform aspect ratios.
 13. The apparatus of claim 9, wherein the imageprocessing module is further configured to: generate one or morepreviews of the image corresponding to one or more effects that can beapplied to the image; and form the one or more previews into a grid ofuniform aspect ratio images.
 14. A non-transitory computer readablemedium for storing computer instructions that, when executed by at leastone processor causes the at least one processor to perform a methodcomprising: determining a representative area of an image, thedetermining comprising: determining an absence of faces in the image;dividing the image into one or more zones; and selecting a zone withmaximum edge strength as the representative area; and generating athumbnail by cropping the image to the representative area.
 15. Thecomputer readable medium of claim 14, wherein determining arepresentative image comprises scaling the image.
 16. The computerreadable medium of claim 15, wherein scaling comprises: determining afirst scale factor as a height of the image divided by a height of asize of a thumbnail image; determining a second scale factor as a widthof the image divided by the width of the thumbnail image; determiningthat the first scale factor is not equal to the second scale factor; andscaling the image using the first and the second scale factor to producea scaled image.
 17. The computer readable medium of claim 14, whereindetermining further comprises: converting the image to a grayscaleimage; applying a filter to produce a smoothed image with reduced noise;detecting edges in the image to produce an edge-detected image; andcomputing a strength of edges for each zone.
 18. The computer readablemedium of claim 14, wherein dividing the image comprises: determiningwhether a first scale factor is greater than a second scale factor;dividing the image horizontally when the first scale factor is greaterthan the second scale factor; and dividing the image vertically when thesecond scale factor is greater than the first scale factor.
 19. Thecomputer readable medium of claim 14, further comprising: generating agrid of cropped images with uniform aspect ratios.
 20. The computerreadable medium of claim 14, further comprising: generating one or morepreviews of the image corresponding to one or more effects that can beapplied to the image; and forming the one or more previews into a gridof uniform aspect ratio images.